Evolution of machine learning in financial risk management: A survey
Financial risk management plays a crucial role in daily financial decision-making, aiming to mitigate risk and maximize profit. Given its reliance on data, financial risk management can greatly benefit from the application of machine learning tools. Over the years, we've observed a clear trend...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04018.pdf |
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| _version_ | 1825206575411757056 |
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| author | Lu Kuan-I |
| author_facet | Lu Kuan-I |
| author_sort | Lu Kuan-I |
| collection | DOAJ |
| description | Financial risk management plays a crucial role in daily financial decision-making, aiming to mitigate risk and maximize profit. Given its reliance on data, financial risk management can greatly benefit from the application of machine learning tools. Over the years, we've observed a clear trend in the evolution of these applications, marked by increasing model complexity and a broader range of manageable tasks. This paper contributes to the field in three key dimensions: First, we provide a clear taxonomy of risks and an introduction to relevant machine learning methods to establish a foundation and identify the targeted issues. Next, we explore real-world data applications, discussing the pros and cons of three methods, from the earliest to the most recent. Finally, based on the observed results, we highlight current challenges and limitations and propose potential directions for improvement. |
| format | Article |
| id | doaj-art-89885f51f7b1491a9c8e965bd587b411 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-89885f51f7b1491a9c8e965bd587b4112025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700401810.1051/itmconf/20257004018itmconf_dai2024_04018Evolution of machine learning in financial risk management: A surveyLu Kuan-I0Actuarial Science, Department of Applied Probability and Statistics, 93117 University of CaliforniaFinancial risk management plays a crucial role in daily financial decision-making, aiming to mitigate risk and maximize profit. Given its reliance on data, financial risk management can greatly benefit from the application of machine learning tools. Over the years, we've observed a clear trend in the evolution of these applications, marked by increasing model complexity and a broader range of manageable tasks. This paper contributes to the field in three key dimensions: First, we provide a clear taxonomy of risks and an introduction to relevant machine learning methods to establish a foundation and identify the targeted issues. Next, we explore real-world data applications, discussing the pros and cons of three methods, from the earliest to the most recent. Finally, based on the observed results, we highlight current challenges and limitations and propose potential directions for improvement.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04018.pdf |
| spellingShingle | Lu Kuan-I Evolution of machine learning in financial risk management: A survey ITM Web of Conferences |
| title | Evolution of machine learning in financial risk management: A survey |
| title_full | Evolution of machine learning in financial risk management: A survey |
| title_fullStr | Evolution of machine learning in financial risk management: A survey |
| title_full_unstemmed | Evolution of machine learning in financial risk management: A survey |
| title_short | Evolution of machine learning in financial risk management: A survey |
| title_sort | evolution of machine learning in financial risk management a survey |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04018.pdf |
| work_keys_str_mv | AT lukuani evolutionofmachinelearninginfinancialriskmanagementasurvey |